Predicting risk of cardiovascular disease using retinal OCT imaging

arXiv:2403.18873v1 Announce Type: cross
Abstract: We investigated the potential of optical coherence tomography (OCT) as an additional imaging technique to predict future cardiovascular disease (CVD). We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images and to capture distinct characteristics of different retinal layers within the OCT image. A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate …

abstract arxiv autoencoders cs.cv cs.lg deep learning disease eess.iv future images imaging learn low optical risk type vae variational autoencoders